Análisis Exploratorio de Datos (EDA) Covid-19

Descripción

Este es un análisis exploratorio de datos con fines didácticos. El análisis es realizado con datos relacionados con Covid-19 reportados por diversos países. Los datos son obtenidos del sitio: https://ourworldindata.org/coronavirus-source-data. El conjunto de datos es actualizado constantemente. Por lo tanto, una ventaja de descargarlo directamente del sitio es obtener los datos actualizados.

Hecho por: Oscar Castro

Fecha de ejecución del codigo:

print(format(Sys.Date(), "%A, %d de %B de %Y"))

## [1] "sábado, 08 de agosto de 2020"

Lectura de datos

covid.df <- read.csv("https://covid.ourworldindata.org/data/owid-covid-data.csv")

Cargamos los paquetes necesarios:

library(ggplot2)
library(knitr)
library(reshape2)
library(kableExtra)

Revisamos la estructura del dataframe:

str(covid.df)

## 'data.frame':    35507 obs. of  36 variables:
##  $ iso_code                       : Factor w/ 212 levels "","ABW","AFG",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ continent                      : Factor w/ 7 levels "","Africa","Asia",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ location                       : Factor w/ 212 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ date                           : Factor w/ 222 levels "2019-12-31","2020-01-01",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ total_cases                    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ new_cases                      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ total_deaths                   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ new_deaths                     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ total_cases_per_million        : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ new_cases_per_million          : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ total_deaths_per_million       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ new_deaths_per_million         : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ new_tests                      : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ total_tests                    : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ total_tests_per_thousand       : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ new_tests_per_thousand         : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ new_tests_smoothed             : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ new_tests_smoothed_per_thousand: num  NA NA NA NA NA NA NA NA NA NA ...
##  $ tests_per_case                 : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ positive_rate                  : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ tests_units                    : Factor w/ 6 levels "","people tested",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ stringency_index               : num  NA 0 0 0 0 0 0 0 0 0 ...
##  $ population                     : num  38928341 38928341 38928341 38928341 38928341 ...
##  $ population_density             : num  54.4 54.4 54.4 54.4 54.4 ...
##  $ median_age                     : num  18.6 18.6 18.6 18.6 18.6 18.6 18.6 18.6 18.6 18.6 ...
##  $ aged_65_older                  : num  2.58 2.58 2.58 2.58 2.58 ...
##  $ aged_70_older                  : num  1.34 1.34 1.34 1.34 1.34 ...
##  $ gdp_per_capita                 : num  1804 1804 1804 1804 1804 ...
##  $ extreme_poverty                : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ cardiovasc_death_rate          : num  597 597 597 597 597 ...
##  $ diabetes_prevalence            : num  9.59 9.59 9.59 9.59 9.59 9.59 9.59 9.59 9.59 9.59 ...
##  $ female_smokers                 : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ male_smokers                   : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ handwashing_facilities         : num  37.7 37.7 37.7 37.7 37.7 ...
##  $ hospital_beds_per_thousand     : num  0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ...
##  $ life_expectancy                : num  64.8 64.8 64.8 64.8 64.8 ...

La descripción de cada una de las variables de este conjunto de datos se encuentra en el sitio: https://github.com/owid/covid-19-data/blob/master/public/data/owid-covid-data-codebook.md.

Ajuste de la columna date que es tipo factor a tipo date:

covid.df$date <- as.Date(covid.df$date, format("%Y-%m-%d"))

Países del conjunto de datos

levels(covid.df$location)

##   [1] "Afghanistan"                      "Albania"                         
##   [3] "Algeria"                          "Andorra"                         
##   [5] "Angola"                           "Anguilla"                        
##   [7] "Antigua and Barbuda"              "Argentina"                       
##   [9] "Armenia"                          "Aruba"                           
##  [11] "Australia"                        "Austria"                         
##  [13] "Azerbaijan"                       "Bahamas"                         
##  [15] "Bahrain"                          "Bangladesh"                      
##  [17] "Barbados"                         "Belarus"                         
##  [19] "Belgium"                          "Belize"                          
##  [21] "Benin"                            "Bermuda"                         
##  [23] "Bhutan"                           "Bolivia"                         
##  [25] "Bonaire Sint Eustatius and Saba"  "Bosnia and Herzegovina"          
##  [27] "Botswana"                         "Brazil"                          
##  [29] "British Virgin Islands"           "Brunei"                          
##  [31] "Bulgaria"                         "Burkina Faso"                    
##  [33] "Burundi"                          "Cambodia"                        
##  [35] "Cameroon"                         "Canada"                          
##  [37] "Cape Verde"                       "Cayman Islands"                  
##  [39] "Central African Republic"         "Chad"                            
##  [41] "Chile"                            "China"                           
##  [43] "Colombia"                         "Comoros"                         
##  [45] "Congo"                            "Costa Rica"                      
##  [47] "Cote d'Ivoire"                    "Croatia"                         
##  [49] "Cuba"                             "Curacao"                         
##  [51] "Cyprus"                           "Czech Republic"                  
##  [53] "Democratic Republic of Congo"     "Denmark"                         
##  [55] "Djibouti"                         "Dominica"                        
##  [57] "Dominican Republic"               "Ecuador"                         
##  [59] "Egypt"                            "El Salvador"                     
##  [61] "Equatorial Guinea"                "Eritrea"                         
##  [63] "Estonia"                          "Ethiopia"                        
##  [65] "Faeroe Islands"                   "Falkland Islands"                
##  [67] "Fiji"                             "Finland"                         
##  [69] "France"                           "French Polynesia"                
##  [71] "Gabon"                            "Gambia"                          
##  [73] "Georgia"                          "Germany"                         
##  [75] "Ghana"                            "Gibraltar"                       
##  [77] "Greece"                           "Greenland"                       
##  [79] "Grenada"                          "Guam"                            
##  [81] "Guatemala"                        "Guernsey"                        
##  [83] "Guinea"                           "Guinea-Bissau"                   
##  [85] "Guyana"                           "Haiti"                           
##  [87] "Honduras"                         "Hong Kong"                       
##  [89] "Hungary"                          "Iceland"                         
##  [91] "India"                            "Indonesia"                       
##  [93] "International"                    "Iran"                            
##  [95] "Iraq"                             "Ireland"                         
##  [97] "Isle of Man"                      "Israel"                          
##  [99] "Italy"                            "Jamaica"                         
## [101] "Japan"                            "Jersey"                          
## [103] "Jordan"                           "Kazakhstan"                      
## [105] "Kenya"                            "Kosovo"                          
## [107] "Kuwait"                           "Kyrgyzstan"                      
## [109] "Laos"                             "Latvia"                          
## [111] "Lebanon"                          "Lesotho"                         
## [113] "Liberia"                          "Libya"                           
## [115] "Liechtenstein"                    "Lithuania"                       
## [117] "Luxembourg"                       "Macedonia"                       
## [119] "Madagascar"                       "Malawi"                          
## [121] "Malaysia"                         "Maldives"                        
## [123] "Mali"                             "Malta"                           
## [125] "Mauritania"                       "Mauritius"                       
## [127] "Mexico"                           "Moldova"                         
## [129] "Monaco"                           "Mongolia"                        
## [131] "Montenegro"                       "Montserrat"                      
## [133] "Morocco"                          "Mozambique"                      
## [135] "Myanmar"                          "Namibia"                         
## [137] "Nepal"                            "Netherlands"                     
## [139] "New Caledonia"                    "New Zealand"                     
## [141] "Nicaragua"                        "Niger"                           
## [143] "Nigeria"                          "Northern Mariana Islands"        
## [145] "Norway"                           "Oman"                            
## [147] "Pakistan"                         "Palestine"                       
## [149] "Panama"                           "Papua New Guinea"                
## [151] "Paraguay"                         "Peru"                            
## [153] "Philippines"                      "Poland"                          
## [155] "Portugal"                         "Puerto Rico"                     
## [157] "Qatar"                            "Romania"                         
## [159] "Russia"                           "Rwanda"                          
## [161] "Saint Kitts and Nevis"            "Saint Lucia"                     
## [163] "Saint Vincent and the Grenadines" "San Marino"                      
## [165] "Sao Tome and Principe"            "Saudi Arabia"                    
## [167] "Senegal"                          "Serbia"                          
## [169] "Seychelles"                       "Sierra Leone"                    
## [171] "Singapore"                        "Sint Maarten (Dutch part)"       
## [173] "Slovakia"                         "Slovenia"                        
## [175] "Somalia"                          "South Africa"                    
## [177] "South Korea"                      "South Sudan"                     
## [179] "Spain"                            "Sri Lanka"                       
## [181] "Sudan"                            "Suriname"                        
## [183] "Swaziland"                        "Sweden"                          
## [185] "Switzerland"                      "Syria"                           
## [187] "Taiwan"                           "Tajikistan"                      
## [189] "Tanzania"                         "Thailand"                        
## [191] "Timor"                            "Togo"                            
## [193] "Trinidad and Tobago"              "Tunisia"                         
## [195] "Turkey"                           "Turks and Caicos Islands"        
## [197] "Uganda"                           "Ukraine"                         
## [199] "United Arab Emirates"             "United Kingdom"                  
## [201] "United States"                    "United States Virgin Islands"    
## [203] "Uruguay"                          "Uzbekistan"                      
## [205] "Vatican"                          "Venezuela"                       
## [207] "Vietnam"                          "Western Sahara"                  
## [209] "World"                            "Yemen"                           
## [211] "Zambia"                           "Zimbabwe"

Continentes del conjunto de datos

levels(covid.df$continent)

## [1] ""              "Africa"        "Asia"          "Europe"       
## [5] "North America" "Oceania"       "South America"

¿Cuál es la cantidad de días máximo que ha sido reportado por los países?

maxdays <- max(table(covid.df$location))
print(paste("Días máximos reportados:",maxdays))

## [1] "Días máximos reportados: 222"

Países que han reportado datos la mayor cantidad de días

names(table(covid.df$location)[table(covid.df$location)==maxdays])

##  [1] "Australia"            "Austria"              "Belarus"             
##  [4] "Belgium"              "Brazil"               "Canada"              
##  [7] "China"                "Croatia"              "Czech Republic"      
## [10] "Denmark"              "Estonia"              "Finland"             
## [13] "France"               "Germany"              "Greece"              
## [16] "Iceland"              "Iran"                 "Israel"              
## [19] "Italy"                "Japan"                "Lithuania"           
## [22] "Luxembourg"           "Malaysia"             "Mexico"              
## [25] "Nepal"                "Netherlands"          "Norway"              
## [28] "Russia"               "Singapore"            "South Korea"         
## [31] "Sweden"               "Switzerland"          "Taiwan"              
## [34] "Thailand"             "United Arab Emirates" "United Kingdom"      
## [37] "United States"        "Vietnam"              "World"

¿En qué fechas inicia y terminan los datos reportados?

startdate <- min(covid.df$date)
enddate <- max(covid.df$date)
print(paste("Inicia el", format(startdate, "%d de %B de %Y"), 
            "y termina el",  format(enddate, "%d de %B de %Y")))

## [1] "Inicia el 31 de diciembre de 2019 y termina el 08 de agosto de 2020"

Rankings de países con las variables totales o acumuladas disponibles en el conjunto de datos

Filtramos nuestro dataframe original, sólo nos quedamos con las observaciones en donde la columna date sea igual a la fecha máxima. De esta manera tendremos los totales o acumulados más actualizados de cada país. Adicionalmente, eliminamos los países con menos de 1 millón de habitantes.

Analizaremos los rankings de países con las siguientes variables:

# Get one row of each country with the most updated totals
covid.total.df <- covid.df[covid.df$date== enddate,]
# Filter countries with less than 1 millón population
covid.total.df <- covid.total.df[covid.total.df$population >= 1000000,]
print(names(covid.total.df)[c(5,7,9,11)])

## [1] "total_cases"              "total_deaths"            
## [3] "total_cases_per_million"  "total_deaths_per_million"

Ranking de países con total de casos confirmados de COVID-19

Para obtener el top 20 ordenamos los datos con la columna total_cases. Guardamos sólo los 20 países con más casos y los 20 países con menos casos. Finalmente, filtramos sólo las columnas que nos interesan. Y mostramos los datos en una tabla y en una gráfica de Barras horizontales.

# Ordenamos los datos de acuerdo a la columna total_cases de manera descendente
ranking.total_cases <- covid.total.df[order(-covid.total.df$total_cases),]
# Quitamos los datos de World, para solo quedarnos con datos de países
ranking.total_cases <- ranking.total_cases[ranking.total_cases$location != "World", ]
# Agregamos una nueva columna con el índice numérico de la posición del pais en el top
ranking.total_cases$position <- 1:nrow(ranking.total_cases)
# Nos quedamos sólo con los primeros 20 países y las columnas que nos interesan
columnfilter <- c("position", "location", "total_cases")
bottom20.total_cases <- tail(ranking.total_cases[, columnfilter],20)
top20.total_cases <- head(ranking.total_cases[, columnfilter],20)
rm(ranking.total_cases)
rownames(top20.total_cases) <- c()
rownames(bottom20.total_cases) <- c()

mexrow <- which(top20.total_cases$location=='Mexico')
tablecolnames <- c("Posición", "País", "Casos Totales")
top20.total_cases$total_cases_formated <- formatC(top20.total_cases$total_cases, 
                                                  format="f", big.mark=",", digits=0)
bottom20.total_cases$total_cases_formated <- formatC(bottom20.total_cases$total_cases, 
                                                     format="f", big.mark=",", digits=0)

kable(top20.total_cases[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, caption="Top 20 - Países con más casos") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "float_left") %>%
  row_spec(mexrow, bold = T, color = "black", background = "yellow")

kable(bottom20.total_cases[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, caption="Top 20 - Países con menos casos") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "right")

Top 20 - Países con más casos

Posición	País	Casos Totales
1	United States	4,941,796
2	Brazil	2,962,442
3	India	2,088,611
4	Russia	877,135
5	South Africa	545,476
6	Mexico	469,407
7	Peru	463,875
8	Chile	368,825
9	Colombia	367,196
10	Iran	322,567
11	United Kingdom	309,005
12	Saudi Arabia	285,793
13	Pakistan	283,487
14	Bangladesh	252,502
15	Italy	249,756
16	Turkey	238,450
17	Argentina	228,182
18	Germany	215,336
19	France	197,921
20	Iraq	144,064

Top 20 - Países con menos casos

Posición	País	Casos Totales
136	Gambia	1,090
137	Syria	1,060
138	Togo	1,028
139	Jamaica	987
140	Chad	942
141	Botswana	909
142	Vietnam	789
143	Lesotho	742
144	Tanzania	509
145	Taiwan	479
146	Burundi	405
147	Myanmar	359
148	Mauritius	344
149	Mongolia	293
150	Eritrea	285
151	Cambodia	246
152	Trinidad and Tobago	225
153	Papua New Guinea	188
154	Timor	25
155	Laos	20

Gráfica de barras horizontales con los 20 países con más casos reportados de COVID-19.

ggplot(data=top20.total_cases, aes(x=reorder(paste(position, location),total_cases), 
                                   y=total_cases, fill=location))+
  geom_bar(stat = "identity", position=position_dodge(), colour="black", show.legend = FALSE)+
  ylab("Número de Casos Reportados de COVID-19") +
  geom_text(aes(y=max(total_cases)+150000, label=total_cases_formated), color="black")+
  labs(title="Top 20 países con más casos reportados de COVID-19")+
  scale_y_continuous(breaks=c(100000, 250000, 500000, 750000, 1000000, 2500000, 4000000, 5000000),
                     label=c("100k", "250k", "500k", "750k", "1m", "2.5m", "4m", "5m"))+
  coord_flip() +
  xlab("Países") +
  theme_bw()+
  theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"))

Ranking de países con total de casos confirmados de COVID-19 - Sólo América:

Obtenemos los rankings pero ahora para el continente americano. Filtramos los datos por la columna continent de “Nort America” y “South America”.

ranking.total_cases.america <- covid.total.df[covid.total.df$continent %in% 
                                                c("North America", "South America") ,]
ranking.total_cases.america <- ranking.total_cases.america[order(-ranking.total_cases.america$total_cases),]
ranking.total_cases.america$position <- 1:nrow(ranking.total_cases.america)
bottom20.total_cases.america <- tail(ranking.total_cases.america[, c("position", "location", "total_cases")],5)
top20.total_cases.america <- head(ranking.total_cases.america[, c("position", "location", "total_cases")],20)
rownames(bottom20.total_cases.america) <- c()
rownames(top20.total_cases.america) <- c()
mexrow <- which(ranking.total_cases.america$location=='Mexico')
rm(ranking.total_cases.america)
top20.total_cases.america$total_cases_formated <- formatC(top20.total_cases.america$total_cases,
                                                          format="f", big.mark=",", digits=0)
bottom20.total_cases.america$total_cases_formated <- formatC(bottom20.total_cases.america$total_cases, 
                                                             format="f", big.mark=",", digits=0)
tablecolnames <- c("Posición", "País", "Casos Totales")
kable(top20.total_cases.america[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, caption="Top 20 América - Países con más casos") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "center") %>%
row_spec(mexrow, bold = T, color = "black", background = "yellow")

Top 20 América - Países con más casos

Posición	País	Casos Totales
1	United States	4,941,796
2	Brazil	2,962,442
3	Mexico	469,407
4	Peru	463,875
5	Chile	368,825
6	Colombia	367,196
7	Argentina	228,182
8	Canada	118,970
9	Ecuador	91,969
10	Bolivia	87,891
11	Dominican Republic	77,709
12	Panama	72,560
13	Guatemala	55,270
14	Honduras	46,365
15	Venezuela	24,166
16	Costa Rica	22,081
17	Puerto Rico	20,686
18	El Salvador	19,544
19	Haiti	7,599
20	Paraguay	6,508

Gráfica de barras horizontales con los 20 países con más casos reportados de COVID-19 de América.

ggplot(data=top20.total_cases.america, aes(x=reorder(paste(position, location),total_cases),
                                           y=total_cases, fill=location))+
  geom_bar(stat = "identity", position=position_dodge(), colour="black", show.legend = FALSE)+
  ylab("Número de Casos Reportados de COVID-19") +
  geom_text(aes(y=max(total_cases)+150000, label=format(total_cases, big.mark=",")), color="black")+
  labs(title="Top 20 países con más casos reportados de COVID-19 de América")+
  scale_y_continuous(breaks=c(100000, 250000, 500000, 750000, 1000000, 2500000, 4000000, 5000000),
                     label=c("100k", "250k", "500k", "750k", "1m", "2.5m", "4m", "5m"))+
  coord_flip() +
  xlab("Países") +
  theme_bw()+
  theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"))

Ranking de países con total de casos confirmados de COVID-19 por millón de habitantes

Para obtener el top 20 ordenamos los datos con la columna total_cases_per_million. Guardamos sólo los 20 países con más casos y los 20 países con menos casos. Finalmente, filtramos sólo las columnas que nos interesan. Y mostramos los datos en una tabla y en una gráfica de Barras horizontales. Cómo México no está en el top 20, lo agregamos al final con la posición correspondiente del ranking.

ranking.total_cases_per_million <- covid.total.df[order(-covid.total.df$total_cases_per_million),]
ranking.total_cases_per_million$position <- 1:nrow(ranking.total_cases_per_million)
columnfilter <- c("position", "location", "total_cases_per_million")
mexico.total_cases_per_million <- ranking.total_cases_per_million[
                                  ranking.total_cases_per_million$location == "México", ]
bottom20.total_cases_per_million <- tail(ranking.total_cases_per_million[, columnfilter],20)
top20.total_cases_per_million <- head(ranking.total_cases_per_million[, columnfilter],20)
mexico.total_cases_per_million <- mexico.total_cases_per_million[, columnfilter]
rm(ranking.total_cases_per_million)
rownames(top20.total_cases_per_million) <- c()
rownames(bottom20.total_cases_per_million) <- c()
rownames(mexico.total_cases_per_million) <- c()


top20.total_cases_per_million <- rbind(top20.total_cases_per_million, mexico.total_cases_per_million)
mexrow <- which(top20.total_cases_per_million$location=='Mexico')
tablecolnames <- c("Posición", "País", "Casos")
top20.total_cases_per_million$total_cases_per_million_formated <- formatC(
  top20.total_cases_per_million$total_cases_per_million, format="f", big.mark=",", digits=2)
bottom20.total_cases_per_million$total_cases_per_million_formated<- formatC(
  bottom20.total_cases_per_million$total_cases_per_million, format="f", big.mark=",", digits=2)

kable(top20.total_cases_per_million[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, caption="Top 20 - Países con más casos por millón de habitantes") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "float_left") %>%
  row_spec(mexrow, bold = T, color = "black", background = "yellow")

kable(bottom20.total_cases_per_million[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, caption="Top 20 - Países con menos casos por millón de habitantes") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "right")

Top 20 - Países con más casos por millón de habitantes

Posición	País	Casos
1	Qatar	39,007.52
2	Bahrain	25,451.01
3	Chile	19,293.84
4	Panama	16,816.66
5	Kuwait	16,561.52
6	Oman	15,874.88
7	United States	14,929.78
8	Peru	14,068.82
9	Brazil	13,937.01
10	Armenia	13,459.96
11	Singapore	9,366.46
12	Israel	9,357.13
13	South Africa	9,197.24
14	Saudi Arabia	8,209.17
15	Sweden	8,151.38
16	Bolivia	7,529.41
17	Belarus	7,261.26
18	Puerto Rico	7,230.74
19	Colombia	7,216.49
20	Dominican Republic	7,163.50

Top 20 - Países con menos casos por millón de habitantes

Posición	País	Casos
137	Eritrea	80.36
138	Mozambique	70.80
139	China	61.54
140	Syria	60.57
141	Yemen	60.22
142	Chad	57.35
143	Burkina Faso	56.21
144	Thailand	47.97
145	Niger	47.63
146	Angola	45.12
147	Burundi	34.06
148	Uganda	27.41
149	Papua New Guinea	21.01
150	Taiwan	20.11
151	Timor	18.96
152	Cambodia	14.71
153	Tanzania	8.52
154	Vietnam	8.11
155	Myanmar	6.60
156	Laos	2.75

Gráfica de barras horizontales con los 20 países con más casos reportados de COVID-19 por millón de habitantes (+ México)

ggplot(data=top20.total_cases_per_million, aes(x=reorder(paste(position, location),total_cases_per_million), 
                                   y=total_cases_per_million, fill=location))+
  geom_bar(stat = "identity", position=position_dodge(), colour="black", show.legend = FALSE)+
  ylab("Número de Casos Reportados de COVID-19") +
  geom_text(aes(y=max(total_cases_per_million)+1250, label=total_cases_per_million_formated), color="black")+
  labs(title="Top 20 países con más casos reportados de COVID-19 por millón de habitantes (+ México)")+
  coord_flip() +
  xlab("Países") +
  theme_bw()+
  theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"))

Ranking de países con total de muertes atribuibles de COVID-19

Para obtener el top 20 ordenamos los datos con la columna total_deaths. Guardamos sólo los 20 países con más casos y los 20 países con menos casos. Filtramos sólo las columnas que nos interesan. Finalmente, mostramos los datos en una tabla y en una gráfica de Barras horizontales.

ranking.total_deaths <- covid.total.df[order(-covid.total.df$total_deaths),]
ranking.total_deaths <- ranking.total_deaths[ranking.total_deaths$location != "World", ]
ranking.total_deaths$position <- 1:nrow(ranking.total_deaths)
columnfilter <- c("position", "location", "total_deaths")
bottom20.total_deaths <- tail(ranking.total_deaths[, columnfilter],20)
top20.total_deaths <- head(ranking.total_deaths[, columnfilter],20)
mexrow <- which(ranking.total_deaths$location=='Mexico')
rm(ranking.total_deaths)
rownames(top20.total_deaths) <- c()
rownames(bottom20.total_deaths) <- c()

tablecolnames <- c("Posición", "País", "Muertes")
top20.total_deaths$total_deaths_formated <- formatC(top20.total_deaths$total_deaths, 
                                                  format="f", big.mark=",", digits=0)
bottom20.total_deaths$total_deaths_formated <- formatC(bottom20.total_deaths$total_deaths, 
                                                     format="f", big.mark=",", digits=0)

kable(top20.total_deaths[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, caption="Top 20 - Países con más muertes atribuibles a COVID-19") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "float_left") %>%
  row_spec(mexrow, bold = T, color = "black", background = "yellow")

kable(bottom20.total_deaths[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, caption="Top 20 - Países con menos muertes atribuibles a COVID-19") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "right")

Top 20 - Países con más muertes atribuibles a COVID-19

Posición	País	Muertes
1	United States	161,356
2	Brazil	99,572
3	Mexico	51,311
4	United Kingdom	46,511
5	India	42,518
6	Italy	35,190
7	France	30,324
8	Peru	20,649
9	Iran	18,132
10	Russia	14,725
11	Colombia	12,250
12	Chile	9,958
13	South Africa	9,909
14	Belgium	9,866
15	Germany	9,195
16	Canada	8,970
17	Netherlands	6,154
18	Pakistan	6,068
19	Ecuador	5,897
20	Turkey	5,813

Top 20 - Países con menos muertes atribuibles a COVID-19

Posición	País	Muertes
136	Namibia	16
137	Mozambique	15
138	Jamaica	13
139	Jordan	11
140	Sri Lanka	11
141	Mauritius	10
142	Vietnam	10
143	Trinidad and Tobago	8
144	Taiwan	7
145	Myanmar	6
146	Uganda	6
147	Rwanda	5
148	Papua New Guinea	3
149	Botswana	2
150	Burundi	1
151	Cambodia	0
152	Eritrea	0
153	Laos	0
154	Mongolia	0
155	Timor	0

Gráfica de barras horizontales con los 20 países con más muertes atribuibles a COVID-19.

ggplot(data=top20.total_deaths, aes(x=reorder(paste(position, location),total_deaths), 
                                   y=total_deaths, fill=location))+
  geom_bar(stat = "identity", position=position_dodge(), colour="black", show.legend = FALSE)+
  ylab("Número de muertes atribuibles a COVID-19") +
  geom_text(aes(y=max(total_deaths)+7500, label=total_deaths_formated), color="black")+
  labs(title="Top 20 países con más muertes atribuibles a COVID-19")+
  scale_y_continuous(breaks=c(10000, 25000, 50000, 75000, 100000, 150000, 170000), 
                     label=c("10k", "25k", "50k", "75k", "100k", "150k", "170k"))+
  coord_flip() +
  xlab("Países") +
  theme_bw()+
  theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"))

Ranking de países con total de muertes atribuibles de COVID-19 por millón de habitantes

Para obtener el top 20 ordenamos los datos con la columna total_deaths_per_million. Guardamos sólo los 20 países con más casos y los 20 países con menos casos. Filtramos sólo las columnas que nos interesan. Finalmente, mostramos los datos en una tabla y en una gráfica de barras horizontales.

ranking.total_deaths_per_million <- covid.total.df[order(-covid.total.df$total_deaths_per_million),]
ranking.total_deaths_per_million$position <- 1:nrow(ranking.total_deaths_per_million)
columnfilter <- c("position", "location", "total_deaths_per_million")
bottom20.total_deaths_per_million <- tail(ranking.total_deaths_per_million[, columnfilter],20)
top20.total_deaths_per_million <- head(ranking.total_deaths_per_million[, columnfilter],20)
mexrow <- which(ranking.total_deaths_per_million$location=='Mexico')
rm(ranking.total_deaths_per_million)
rownames(top20.total_deaths_per_million) <- c()
rownames(bottom20.total_deaths_per_million) <- c()

tablecolnames <- c("Posición", "País", "Muertes")
top20.total_deaths_per_million$total_deaths_per_million_formated <- formatC(
  top20.total_deaths_per_million$total_deaths_per_million, format="f", big.mark=",", digits=2)
bottom20.total_deaths_per_million$total_deaths_per_million_formated <- formatC(
  bottom20.total_deaths_per_million$total_deaths_per_million, format="f", big.mark=",", digits=2)

kable(top20.total_deaths_per_million[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, 
      caption="Top 20 - Países con más muertes atribuibles a COVID-19 por millón de habitantes") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "float_left") %>%
  row_spec(mexrow, bold = T, color = "black", background = "yellow")

kable(bottom20.total_deaths_per_million[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, 
      caption="Top 20 - Países con menos muertes atribuibles a COVID-19 por millón de habitantes") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "right")

Top 20 - Países con más muertes atribuibles a COVID-19 por millón de habitantes

Posición	País	Muertes
1	Belgium	851.28
2	United Kingdom	685.13
3	Peru	626.26
4	Italy	582.02
5	Sweden	570.63
6	Chile	520.92
7	United States	487.48
8	Brazil	468.44
9	France	464.57
10	Mexico	397.97
11	Panama	368.73
12	Netherlands	359.15
13	Ireland	358.87
14	Ecuador	334.24
15	Bolivia	301.89
16	Armenia	262.21
17	Macedonia	249.11
18	Colombia	240.75
19	Canada	237.66
20	Kyrgyzstan	223.78

Top 20 - Países con menos muertes atribuibles a COVID-19 por millón de habitantes

Posición	País	Muertes
137	Nepal	2.40
138	Angola	1.95
139	Jordan	1.08
140	Botswana	0.85
141	Thailand	0.83
142	Sri Lanka	0.51
143	Mozambique	0.48
144	Rwanda	0.39
145	Tanzania	0.35
146	Papua New Guinea	0.34
147	Taiwan	0.29
148	Uganda	0.13
149	Myanmar	0.11
150	Vietnam	0.10
151	Burundi	0.08
152	Cambodia	0.00
153	Eritrea	0.00
154	Laos	0.00
155	Mongolia	0.00
156	Timor	0.00

Gráfica de barras horizontales con los 20 países con más muertes atribuibles a COVID-19 por millón de habitantes.

ggplot(data=top20.total_deaths_per_million, aes(x=reorder(paste(position, location),total_deaths_per_million), 
                                   y=total_deaths_per_million, fill=location))+
  geom_bar(stat = "identity", position=position_dodge(), colour="black", show.legend = FALSE)+
  ylab("Número de muertes atribuibles a COVID-19 por millón de habitantes") +
  geom_text(aes(y=max(total_deaths_per_million)+25, label=total_deaths_per_million_formated), color="black")+
  labs(title="Top 20 países con más muertes atribuibles a COVID-19 por millón de habitantes")+
  coord_flip() +
  xlab("Países") +
  theme_bw()+
  theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"))

Tendencias

Tendencias de nuevos contagios de los 5 países con más casos reportados de COVID-19 + México

Gráfica creada desde la fecha inicial del conjunto de datos:

plot.trend.new_cases <- function(startdate, enddate, countries, graphtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=new_cases, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "month", date_labels = "%B")+
    ggtitle(graphtitle)+
    ylab("Nuevos casos de COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
          axis.text.y = element_text(size=11, face="bold", colour = "black"), 
          axis.text.x = element_text(size=11, face="bold", colour = "black"),
          legend.position = "bottom",
          legend.title = element_text(size = 14),
          legend.text = element_text(size = 13),
          legend.key.width = unit(1.5,"cm"))+
    guides(colour = guide_legend(override.aes = list(size=2)))
}
top5.total_cases <- c(head(as.character(top20.total_cases$location),5), "México")
plot.trend.new_cases(startdate, enddate, top5.total_cases, "Tendencia de contagios de Covid-19 por países de enero a agosto")

Podemos observar que las líneas empiezan a subir a partir de marzo. Modificamos la gráfica con diversas fechas de inicio. Por ejemplo, con los datos a partir de marzo:

plot.trend.new_cases("2020-03-01", enddate, top5.total_cases, "Tendencia de contagios de Covid-19 por países de marzo a agosto")

plot.trend.new_cases("2020-06-01", enddate, top5.total_cases, "Tendencia de contagios de Covid-19 por países de junio a agosto")

plot.trend.new_cases("2020-07-01", enddate, top5.total_cases, "Tendencia de contagios de Covid-19 por países de julio a agosto")

plot.trend.new_cases <- function(startdate, enddate, countries, graphtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=new_cases, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "day", date_labels = "%b %d")+
    ggtitle(graphtitle)+
    ylab("Nuevos casos de COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
          axis.text.y = element_text(size=11, face="bold", colour = "black"), 
          axis.text.x = element_text(size=11, face="bold", colour = "black"),
          legend.position = "bottom",
          legend.title = element_text(size = 14),
          legend.text = element_text(size = 13),
          legend.key.width = unit(1.5,"cm"))+
    guides(colour = guide_legend(override.aes = list(size=2)))
}
plot.trend.new_cases(enddate-14, enddate, top5.total_cases, "Tendencia de contagios de Covid-19 por países de las últimas dos semanas")

Tendencias de nuevas muertes por covid de los 5 países con más casos reportados + México

Gráfica creada desde la fecha inicial del conjunto de datos:

plot.trend.new_deaths <- function(startdate, enddate, countries, graphtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=new_deaths, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "month", date_labels = "%B")+
    ggtitle(graphtitle)+
    ylab("Nuevos muertes atribuibles a COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
          axis.text.y = element_text(size=11, face="bold", colour = "black"), 
          axis.text.x = element_text(size=11, face="bold", colour = "black"),
          legend.position = "bottom",
          legend.title = element_text(size = 14),
          legend.text = element_text(size = 13),
          legend.key.width = unit(1.5,"cm"))+
    guides(colour = guide_legend(override.aes = list(size=2)))
}
top5.total_cases <- c(head(as.character(top20.total_cases$location),5), "México")
plot.trend.new_deaths(startdate, enddate, top5.total_cases, "Tendencia de nuevas muertes atribuidas a Covid-19 por países de enero a agosto")

Gráfica con los datos a partir de marzo:

plot.trend.new_deaths("2020-03-01", enddate, top5.total_cases, "Tendencia de nuevas muertes atribuidas a Covid-19 de marzo a agosto")

plot.trend.new_deaths("2020-06-01", enddate, top5.total_cases, "Tendencia de nuevas muertes atribuidas a Covid-19 por países de junio a agosto")

plot.trend.new_deaths("2020-07-01", enddate, top5.total_cases, "Tendencia de nuevas muertes atribuidas a Covid-19 por países de julio a agosto")

plot.trend.new_deaths <- function(startdate, enddate, countries, graphtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=new_deaths, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "day", date_labels = "%b %d")+
    ggtitle(graphtitle)+
    ylab("Nuevos casos de COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"),
        legend.position = "bottom",
        legend.title = element_text(size = 14),
        legend.text = element_text(size = 13),
        legend.key.width = unit(1.5,"cm"))+
        guides(colour = guide_legend(override.aes = list(size=2)))
}
plot.trend.new_deaths(enddate-14, enddate, top5.total_cases, "Tendencia de nuevas muertes atribuidas a Covid-19 por países de las últimas dos semanas")

Curvas de contagios acumulados de COVID-19 de los 5 países con más casos reportados + México

plot.trend.total_cases <- function(startdate, enddate, countries, graphtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=total_cases, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "month", date_labels = "%B")+
    ggtitle(graphtitle)+
    ylab("Casos acumulados de COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"),
        legend.position = "bottom",
        legend.title = element_text(size = 14),
        legend.text = element_text(size = 13),
        legend.key.width = unit(1.5,"cm"))+
        guides(colour = guide_legend(override.aes = list(size=2)))
}
plot.trend.total_cases(startdate, enddate, top5.total_cases, "Curvas de contagios acumulados de COVID-19 de los 5 países con más contagios reportados ")

Gráfica con los datos a partir de marzo:

plot.trend.total_cases("2020-03-01", enddate, top5.total_cases, "Curvas de contagios acumulados de COVID-19 de los 5 países con más contagios reportados de marzo a agosto")

plot.trend.total_cases("2020-06-01", enddate, top5.total_cases, "Curvas de contagios acumulados de COVID-19 de los 5 países con más contagios reportados de junio a agosto")

plot.trend.total_cases("2020-07-01", enddate, top5.total_cases, "Curvas de contagios acumulados de COVID-19 de los 5 países con más contagios reportados de julio a agosto")

La cantidad de contagios que tienen Estados Unidos y Brasil no me permiten apreciar las curvas del resto de los países. Quitamos a esos 2 países y agregamos a los 2 países que sigan en el top 20 de países con más contagios reportados.

total_cases.others <- as.character(top20.total_cases$location)[3:7]
plot.trend.total_cases("2020-07-01", enddate, total_cases.others, "Curvas de contagios acumulados de COVID-19 reportados de julio a agosto")

plot.trend.total_cases <- function(startdate, enddate, countries, graphtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=total_cases, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "day", date_labels = "%b %d")+
    ggtitle(graphtitle)+
    ylab("Casos acumulados de COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"),
        legend.position = "bottom",
        legend.title = element_text(size = 14),
        legend.text = element_text(size = 13),
        legend.key.width = unit(1.5,"cm"))+
        guides(colour = guide_legend(override.aes = list(size=2)))
}
plot.trend.total_cases(enddate-14, enddate, total_cases.others, "Curvas de contagios acumulados de COVID-19 reportados las últimas dos semanas")

Quitamos a India y Rusia, para ver más a detalle las curvas de contagio acumulado de México, perú y Sudáfrica de las últimas dos semanas:

total_cases.others <- as.character(top20.total_cases$location)[3:7]
total_cases.others <- total_cases.others[c(-1,-2)]
countrieslabels <- paste(total_cases.others, collapse = ', ')
plot.trend.total_cases(enddate-14, enddate, total_cases.others, paste("Curvas de contagios acumulados de COVID-19 reportados las últimas dos semanas",countrieslabels))

Curvas de muertes acumuladas por COVID-19 de los 5 países con más muertes reportadas + México

plot.trend.total_deaths <- function(startdate, enddate, countries, graphtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=total_deaths, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "month", date_labels = "%B")+
    ggtitle(graphtitle)+
    ylab("Muertes acumulados por COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"),
        legend.position = "bottom",
        legend.title = element_text(size = 14),
        legend.text = element_text(size = 13),
        legend.key.width = unit(1.5,"cm"))+
        guides(colour = guide_legend(override.aes = list(size=2)))
}
top5.total_deaths <-  head(as.character(top20.total_deaths$location),5)
plot.trend.total_deaths(startdate, enddate, top5.total_deaths, "Curvas de muertes acumuladas de COVID-19 de los 5 países con más muertes reportadas en total ")

Gráfica con los datos a partir de marzo:

plot.trend.total_deaths("2020-03-01", enddate, top5.total_deaths, "Curvas de muertes acumuladas por COVID-19 de marzo a agosto de los 5 países con más muertes reportadas en total")

plot.trend.total_deaths("2020-06-01", enddate, top5.total_deaths, "Curvas de muertes acumuladas por COVID-19 de junio a agosto de los 5 países con más muertes reportadas en total")

plot.trend.total_deaths("2020-07-01", enddate, top5.total_deaths, "Curvas de muertes acumuladas por COVID-19 de julio a agosto de los 5 países con más muertes reportadas en total")

La cantidad de contagios que tienen Estados Unidos y Brasil no permiten apreciar las curvas del resto de los países. Quitamos a esos 2 países y agregamos a los 2 países que sigan en el top 20 de países con más contagios reportados.

total_deaths.others <- as.character(top20.total_deaths$location)[3:7]
plot.trend.total_deaths("2020-07-01", enddate, total_deaths.others, "Curvas de contagios acumulados de COVID-19 reportados de julio a agosto")

plot.trend.total_deaths <- function(startdate, enddate, countries, graphtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=total_deaths, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "day", date_labels = "%b %d")+
    ggtitle(graphtitle)+
    ylab("Muertes acumulados por COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"),
        legend.position = "bottom",
        legend.title = element_text(size = 14),
        legend.text = element_text(size = 13),
        legend.key.width = unit(1.5,"cm"))+
        guides(colour = guide_legend(override.aes = list(size=2)))
}
plot.trend.total_deaths(enddate-14, enddate, total_deaths.others, "Curvas de contagios acumulados de COVID-19 reportados de las últimas dos semanas")

Las curvas de Francia, Italia y Reino Unido están planas, se ha estancado la cantidad de muertes atribuidas a COVID-19. Una posible razón es que la pandemia inició primero en Europa. Las curvas de México e India siguen en aumento. Quitamos esos países y agregamos otros en donde al parecer la cantidad de muertes atribuidas a COVID-19 aún sigue en aumento, y generamos la gráfica de las curvas de las últimas 2 semanas sin contar a Estados Unidos y Brasil:

total_deaths.others <- as.character(top20.total_deaths$location)[c(3,5,8,9,10)]
countrieslabels <- paste(total_deaths.others, collapse = ', ')
plot.trend.total_deaths(enddate-14, enddate, total_deaths.others, paste("Curvas de muertes acumuladas por COVID-19 reportadas las últimas dos semanas:",countrieslabels))